摘要
对放射性核素进行能谱分析,应正确识别谱信号,降低因统计涨落产生的误差.因传统去噪手段存在不可避免的缺陷,使用小波分析法取代传统方法应用于γ能谱去噪.以信噪比和均方根误差为衡量标准,仿真实验验证了小波去噪法的优越性.对现场采集到的能谱进行小波去噪处理,结果显示,小波去噪法可以有效去除噪声及假峰干扰,为寻峰等后续处理提供便利.
In order to analyze radionuclide spectra,accurate signals should be identified as well as the errors caused by statistical fluctuation should be reduced.Because traditional denoising methods have inevitable defects,wavelet method analysis is applied intoγray spectra denoising instead of traditional methods.Taking signal-to-noise ratio（SNR）and root mean square error as measurement criteria,the superiorities of wavelet denoising method were verified by simulation experiments.The gathered spectra from fields was processed by wavelet denoising method,results showed that wavelet denoising method could eliminate noises effectively,remove disturbance form ghost peak and provide more convenience for peak searching.
出处
《上海应用技术学院学报（自然科学版）》
2016年第1期99-102,共4页
Journal of Shanghai Institute of Technology: Natural Science
关键词
小波分析
能谱去噪
信噪比
均方根误差
wavelet analysis
spectra denoising
signal-to-noise ratio
root mean square error
